Game Theory Based Correlated Privacy Preserving Analysis in Big Data

Wu, Xiaotong and Wu, Taotao and Khan, Maqbool and Ni, Qiang and Dou, Wanchun (2017) Game Theory Based Correlated Privacy Preserving Analysis in Big Data. IEEE Transactions on Big Data. ISSN 2332-7790

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Abstract

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors’ privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Big Data
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subjects:
ID Code:
86592
Deposited By:
Deposited On:
05 Jun 2017 07:52
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Sep 2020 03:18